STEM Imaging Research Articles

Formation of Sn/Zn alloy or core-shell nanoparticles via pulsed nanosecond discharges in liquid toluene

In the exploration of new techniques for the synthesis of metallic nanoparticles, the possibility to exploit electrical discharges in liquid has arisen as an easy, high throughput and low-cost method. This technique of synthesis offers an extensive playground to produce a wide range of nanostructures with composition highly dependent on that of the electrode and of the liquid medium. Here, we demonstrate the formation of a Sn–Zn nanoalloy (particle diameter <10 nm) using electrical discharge between a Sn anode and a Zn cathode immersed in liquid toluene. Core/shell nanoparticles, with diameter between 12 and 20 nm, are also produced. These particles are composed of a Sn crystalline core and a shell made of Zn, Sn and O. A third class of particles was also found, although being rarer, constituted of large (hundreds of nm) Sn particles, with a thin Sn oxide shell. Detailed structural and chemical characterizations were accomplished via TEM and STEM imaging, as well as STEM-EDX analyses on the single nanoparticles and, considering the complex variety of phenomena taking place in in-liquid plasma, a plausible mechanism of synthesis is proposed.

Revealing Pre-service Teachers' Mind Maps on STEM Education through STEM Images

STEM education (based on the integration of the science, technology, engineering and mathematics disciplines) has recently been an integral part of education system and curriculum in many countries aiming to be more technologically competitive in this age of innovation. While the importance of STEM education has gradually increasing in education system with the complexities of today’s world, revealing pre-service teachers’ mind maps on STEM education and preparing them for STEM education is a crucial issue since the readiness of the teachers affects the quality of education. In this context, the aim of this study is to reveal pre-service science and mathematics teachers’ mind maps on STEM Education through STEM images. The research group consists of 6 pre-service teachers (3 of them are pre-service science teachers (PST) and 3 of them are pre-service mathematics teachers (PMT)) who are 4th grade level students from a public university in Istanbul, Turkey. In current research, case study design was used. The data of study were collected through drawings and focus group discussion. The data were analyzed with thematic analysis through Integrated Teaching (IT) Framework. As a result, 41 codes about pre-service teachers’ mind maps on STEM education were determined under 14 categories of 5 themes of IT Framework. The results of this study are crucial in terms of having potential to guide educational policy makers, curriculum developers, researchers and in-service and pre-service teachers about STEM education.

Unsupervised learning of ferroic variants from atomically resolved STEM images

An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and is shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in this article is available at https://github.com/saimani5/ferroelectric_domains_rVAE.

Element Specific Atom Counting at the Atomic Scale by Combining High Angle Annular Dark Field Scanning Transmission Electron Microscopy and Energy Dispersive X‐ray Spectroscopy

A new methodology is presented to count the number of atoms in multimetallic nanocrystals by combining energy dispersive X-ray spectroscopy (EDX) and high angle annular dark field scanning transmission electron microscopy (HAADF STEM). For this purpose, the existence of a linear relationship between the incoherent HAADF STEM and EDX images is exploited. Next to the number of atoms for each element in the atomic columns, the method also allows quantification of the error in the obtained number of atoms, which is of importance given the noisy nature of the acquired EDX signals. Using experimental images of an Au@Ag core-shell nanorod, it is demonstrated that 3D structural information can be extracted at the atomic scale. Furthermore, simulated data of an Au@Pt core-shell nanorod show the prospect to characterize heterogeneous nanostructures with adjacent atomicnumbers.

Phase Separation in a Novel Selective Lithium Extraction from Citrate Media with D2EHPA

Lithium-ion battery (LIB) recycling has received continued interest in recent years due to its benefits, which include reducing the environmental impact of spent LIBs and providing a secondary source of valuable metals, such as Li, Co, and Ni. This paper characterized the Li separation with D2EHPA from citrate media as a function of pH and identified the optimal overall Li separation at a pH of 5.5. The Li separation was optimized at a pH of 5.5, with which it was concluded that 23 vol.% D2EHPA and an O/A ratio of 4 provided the best Li separation, for which 66.1% Li was extracted with 26.9% residual Mn, 6.8% Co, and 7.7% Ni in a single stage. The formation of a reversible hydrophobic third phase was identified during Li extraction at a pH of 5.5 or greater. Investigation of the third phase revealed that more than 99% of the Li, Co, and Ni were extracted to the third phase, while more than 69% of the Mn was extracted to the organic phase. STEM images of the third phase revealed a honeycomb-like structure, which was hypothesized to be a 2D mesoporous film caused by the insolubility of the organometallic complexes in the aqueous and organic phase.

Structure of the active ingredient of Pepto-Bismol by 3D ED and STEM imaging

Structure of the active ingredient of Pepto-Bismol by 3D ED and STEM imaging

Precessed electron diffraction study of defects and strain in GaN nanowires fabricated by top-down etching

Gallium nitride (GaN) nanowires (NWs) have been fabricated by top-down etching from GaN heteroepitaxial films, which provides an accurate control of their position and dimensions. However, these NWs contain, similar to the initial GaN films, high density of structural defects such as threading dislocations (TDs). In this work, different strategies to reduce the density of defects along the NWs have been compared based on two different wet etching approaches followed by a rapid thermal annealing (RTA) at 750 °C. The addition of a 30 nm SiNx coating is also explored. The defects and strain/stress along the NWs have been studied by high resolution transmission electron microscopy, diffraction contrast imaging in two-beam conditions and 4D STEM, as well as strain maps calculated from scanning precession electron diffraction measurements. RTA reduced the density of TDs at the middle of GaN NWs with bare surfaces by approximately 25%. The reduction increased to approximately 70% by RTA of GaN NWs with surfaces coated by amorphous SiNx, which is attributed to enhancement of dislocation movements by stresses induced from differential thermal expansion of GaN and SiNx. These results suggest a process route that, if optimized and combined with reduction of NW diameter, could establish etching as an efficient fabrication method for high crystal quality GaN NWs.

Deep ensemble learning for automatic medicinal leaf identification

The therapeutic nature of medicinal plants and their ability to heal many diseases raises the need for their automatic identification. Different parts of plants that help in their identification include root, fruit, bark, stem but leaf images have been widely used as they are an abundant source of information and are also easily available. This work explores the branch of Artificial Intelligence, called deep learning, and proposes an Ensemble learning approach to rapidly detect medicinal plants using the leaf image. The medicinal leaf dataset consists of 30 classes. Transfer learning approach was used to initialize the parameters and pre-train Neural networks namely MobileNetV2, InceptionV3, and ResNet50. These component models were used to extract features from the input images and the softmax layer connected to the Dense Layer was used as the classifier to train the models on the concerned dataset. The obtained accuracies were validated using threefold and fivefold cross-validation. The Ensemble Deep Learning- Automatic Medicinal Leaf Identification (EDL-AMLI) classifier based on the weighted average of the component model outputs was used as the final classifier. It was observed that the EDL-AMLI outperformed the state-of-the-art pre-trained models such as MobileNetV2, InceptionV3, and ResNet50 by achieving 99.66% accuracy on the test set and average accuracy of 99.9% using threefold and fivefold cross validation.

Carbon Quantum Dots for Stem Cell Imaging and Deciding the Fate of Stem Cell Differentiation

Nanotechnology advancements and applications have paved the way for new possibilities in regenerative medicine and tissue engineering. It is a relatively new field that has the potential to improve stem cell differentiation and therapy greatly. Numerous studies have demonstrated that nanomaterials can function as a physiological niche for the formation and differentiation of stem cells. However, quantum dots (QDs), such as carbon quantum dots (CQDs) and graphene quantum dots (GQDs), have shown considerable promise in the field of regenerative medicine. To date, most research has focused on stem cell tracking and imaging using CQDs. However, their interaction with stem cells and the associated possibility for differentiation by selectively focusing chemical signals to a particular lineage has received scant attention. In this mini-review, we attempt to categorize a few pathways linked with the role of CQDs in stem cell differentiation.

Atomic-scale imaging of dopant atoms in Si-doped GaN

Determination of the concentration and distribution of trace impurity is a key factor in the manufacture of advanced semiconductor materials and is of great significance to the development of sophisticated electronics. Doping has a great influence on the performance of GaN, and a small change in the impurity content will lead to the degradation of the device performance, or even cause device failure. Determining the trace impurity and spatial distribution is therefore critical to understanding and adequately tuning functional properties. In this study, the two-dimensional distribution of Si-doped in GaN was determined by atomic resolution scanning transmission electron microscopy high angle annular dark field (STEM-HAADF) image combined with EDS mapping. Furthermore, STEM image simulation was used to estimate the distribution of trace impurity in the depth direction. This method may be useful for the determination of trace impurity concentration and distribution in semiconductor materials.

Gated Dense Convolutional Neural Networks for Unbalanced Representations in STEM Tomography

Gated Dense Convolutional Neural Networks for Unbalanced Representations in STEM Tomography

STEM Imaging, Monochromated EELS, and Theory of Natural and Artificial Superlattices

STEM Imaging, Monochromated EELS, and Theory of Natural and Artificial Superlattices

Layer Stacking Determination in Topological Semimetal MoTe2 via STEM Imaging, Liquid He TEM, and Quantitative Electron Diffraction

Layer Stacking Determination in Topological Semimetal MoTe2 via STEM Imaging, Liquid He TEM, and Quantitative Electron Diffraction

Atomic Resolution STEM Imaging of Novel Van der Waals Materials Synthesized by Soft Chemical Methods

Atomic Resolution STEM Imaging of Novel Van der Waals Materials Synthesized by Soft Chemical Methods

Optimizing STEM Imaging Conditions Towards Reliable Representation of Single Atom Catalysts

Optimizing STEM Imaging Conditions Towards Reliable Representation of Single Atom Catalysts

Automation of Supported Nanoparticle Recognition in Low Contrast STEM Images

Automation of Supported Nanoparticle Recognition in Low Contrast STEM Images

Combining STEM Imaging and X-Ray Diffraction for Structure Determination of a New Highly Distorted Infinite-Layer Phase

Combining STEM Imaging and X-Ray Diffraction for Structure Determination of a New Highly Distorted Infinite-Layer Phase

Scanning Transmission Electron Microscopy in a Scanning Electron Microscope for the High-Throughput Imaging of Biological Assemblies

Electron microscopy of soft and biological materials, or "soft electron microscopy", is essential to the characterization of macromolecules. Soft microscopy is governed by enhancing contrast while maintaining low electron doses, and sample preparation and imaging methodologies are driven by the length scale of features of interest. While cryo-electron microscopy offers the highest resolution, larger structures can be characterized efficiently and with high contrast using low-voltage electron microscopy by performing scanning transmission electron microscopy in a scanning electron microscope (STEM-in-SEM). Here, STEM-in-SEM is demonstrated for a four-lobed protein assembly where the arrangement of the proteins in the construct must be examined. STEM image simulations show the theoretical contrast enhancement at SEM-level voltages for unstained structures, and experimental images with multiple STEM modes exhibit the resolution possible for negative-stained proteins. This technique can be extended to complex protein assemblies, larger structures such as cell sections, and hybrid materials, making STEM-in-SEM a valuable high-throughput imaging method.

Automated identification of hip arthroplasty implants using artificial intelligence

The purpose of this study was to develop and evaluate the performance of deep learning methods based on convolutional neural networks (CNN) to detect and identify specific hip arthroplasty models. In this study, we propose a novel deep learning-based approach to identify hip arthroplasty implants’ design using anterior–posterior images of both the stem and the cup. We harness the pre-trained ResNet50 CNN model and employ transfer learning methods to adapt the model for the implants identification task using a total of 714 radiographs of 4 different hip arthroplasty implant designs. Performance was compared with the operative notes and crosschecked with implant sheets. We also evaluate the difference in performance of models trained with the images of the stem, the cup or both. The training and validation data sets were comprised of 357 stem images and 357 cup radiographs across 313 patients and included 4 hip arthroplasty implants from 4 leading implant manufacturers. After 1000 training epochs the model classified 4 implant models with very high accuracy. Our results showed that jointly using stem images and cup images did not improve the classification accuracy of the CNN model. CNN can accurately distinguish between specific hip arthroplasty designs. This technology could offer a useful adjunct to the surgeon in preoperative identification of the prior implant. Using stem images or cup images to train the CNN can both achieve effective identification accuracy, with the accuracy of the stem images being higher. Using stem images and cup images together is not more effective than using images from only one perspective.

Hydroxyapatite Decorated with Tungsten Oxide Nanoparticles: New Composite Materials against Bacterial Growth.

The availability of biomaterials able to counteract bacterial colonization is one of the main requirements of functional implants and medical devices. Herein, we functionalized hydroxyapatite (HA) with tungsten oxide (WO3) nanoparticles in the aim to obtain composite materials with improved biological performance. To this purpose, we used HA, as well as HA functionalized with polyacrilic acid (HAPAA) or poly(ethylenimine) (HAPEI), as supports and polyvinylpyrrolidone (PVP) as stabilizing agent for WO3 nanoparticles. The number of nanoparticles loaded on the substrates was determined through Molecular Plasma-Atomic Emission Spectroscopy and is quite small, so it cannot be detected through X-ray diffraction analysis. It increases from HAPAA, to HA, to HAPEI, in agreement with the different values of zeta potential of the different substrates. HRTEM and STEM images show the dimensions of the nanoparticles are very small, less than 1 nm. In physiological solution HA support displays a greater tungsten cumulative release than HAPEI, despite its smaller loaded amount. Indeed, WO3 nanoparticles-functionalized HA exhibits a remarkable antibacterial activity against the Gram-positive Staphylococcus aureus in absence of cytotoxicity, which could be usefully exploited in the biomedical field.